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| Title |
New statistical methods for estimation of recombination fractions in F2 population
|
|---|---|
| Published in |
BMC Bioinformatics, October 2017
|
| DOI | 10.1186/s12859-017-1804-8 |
| Pubmed ID | |
| Authors |
Yuan-De Tan, Xiang H. F. Zhang, Qianxing Mo |
| Abstract |
Dominant markers in an F2 population or a hybrid population have much less linkage information in repulsion phase than in coupling phase. Linkage analysis produces two separate complementary marker linkage maps that have little use in disease association analysis and breeding. There is a need to develop efficient statistical methods and computational algorithms to construct or merge a complete linkage dominant marker maps. The key for doing so is to efficiently estimate recombination fractions between dominant markers in repulsion phases. We proposed an expectation least square (ELS) algorithm and binomial analysis of three-point gametes (BAT) for estimating gamete frequencies from F2 dominant and codominant marker data, respectively. The results obtained from simulated and real genotype datasets showed that the ELS algorithm was able to accurately estimate frequencies of gametes and outperformed the EM algorithm in estimating recombination fractions between dominant loci and recovering true linkage maps of 6 dominant loci in coupling and unknown linkage phases. Our BAT method also had smaller variances in estimation of two-point recombination fractions than the EM algorithm. ELS is a powerful method for accurate estimation of gamete frequencies in dominant three-locus system in an F2 population and BAT is a computationally efficient and fast method for estimating frequencies of three-point codominant gametes. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 10 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 4 | 40% |
| Researcher | 2 | 20% |
| Student > Bachelor | 1 | 10% |
| Professor | 1 | 10% |
| Unknown | 2 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 5 | 50% |
| Biochemistry, Genetics and Molecular Biology | 2 | 20% |
| Unknown | 3 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 January 2018.
All research outputs
#20,459,801
of 23,016,919 outputs
Outputs from BMC Bioinformatics
#6,891
of 7,316 outputs
Outputs of similar age
#281,806
of 323,053 outputs
Outputs of similar age from BMC Bioinformatics
#94
of 105 outputs
Altmetric has tracked 23,016,919 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,316 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 105 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.